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This work proposes a novel scheme for encrypted JPEG image retrieval, which includes image encryption and unsupervised/supervised retrieval phases. Using this scheme, the encrypted images are produced by permuting DCT coefficients, and transmitted to a database server. With an encrypted query image, although the server does not know the plaintext content, he may get the histogram at each frequency position. After calculating the distances between the histograms of encrypted query image and database image, the server can return the encrypted images with plaintext content similar to the query image according to integrated distances. If a training image set is available, the retrieval results can be also determined by conditional probabilities calculated from a supervised mechanism.

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Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method’s sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm’s stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.

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